Method for recommendation of audio

ABSTRACT

A method for recommendation of a content item, comprising: providing first identifiers for first content items stored on a first device; providing second identifiers for second content items stored on a second device; determining measures for at least a part of said first content items, wherein each measure is associated with a respective content item, and said measure depends on said first and second identifiers; determining or updating, on a third device, an item-to-item matrix based on said measures, wherein said first, second, and third devices are physically separated devices; determining a recommendation for a user based on said item-to-item matrix.

An embodiment of the invention relates to a method for recommendation ofaudio to a user. A further embodiment of the invention relates to adevice for content recommendation. A still further embodiment of theinvention relates to a server for generating a recommendation.

BACKGROUND

Today, large audio data bases, e.g. comprising millions of songs ormusic videos, exist. It is a challenging task for a user to findaudio/video data matching his taste.

It is an object of embodiments described in the following to providerecommendations to a user.

This object is solved by a method and respective devices as defined inclaims 1, 8, and 15.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are included to provide a furtherunderstanding of embodiments and are incorporated in and constitute apart of this specification. The drawings illustrate embodiments andtogether with the description serve to explain principles ofembodiments. Other embodiments and many of the intended advantages ofembodiments will be readily appreciated as they become better understoodby reference to the following detailed description. The elements of thedrawings are not necessarily to scale relative to each other. Likereference numerals designate corresponding similar parts.

FIG. 1 is a block diagram illustrating an embodiment of a method forrecommendation.

FIG. 2 is a further block diagram illustrating the interaction ofdifferent devices according to further embodiment.

FIGS. 3A and 3B illustrate an embodiment of the method forrecommendation and focuses on the calculation of measures for contentitems.

FIGS. 4A and 4B illustrate a further embodiment of a method forrecommendation where measures for content items depend on favoritecontent items of a user.

FIG. 5 illustrates an embodiment where only tags of content items of afavorite list are exchanged.

FIG. 6 illustrates a further embodiment where additionally to tags, userdata between devices are exchanged.

FIG. 7 illustrates how an item-to-item matrix is used to make arecommendation.

FIG. 8 shows a device for content recommendation.

FIG. 9 shows a server device for generating a content recommendation andsending the recommendation to a user device.

DETAILED DESCRIPTION

In the following, embodiments are described. It is important to notethat all described embodiments in the following may be combined in anyway, i.e. there is no limitation that certain described embodiments maynot be combined with others. Further, it should be noted that the samereference signs throughout the Figures denote the same or similarelements.

In FIG. 1, at 100, first identifiers for first content items areprovided. The first identifiers may be calculated for the content itemson a first device.

At 102, second identifiers for second content items are provided. Thesecond identifiers may be calculated at a second device.

“Content items” may be pieces of audio or other multimedia items. Forexample, content items may be songs or music videos or other videosstored on the first and second devices.

The first and second devices may be portable devices and physicallyseparated devices.

At 104, measures are determined for at least a part of the first contentitems, wherein each measure is associated with a respective contentitem, and the measure depends on the first and second identifiers. Theterm “measure” may also be referred to as “score”. In an embodiment, themeasures may describe a degree of similarity of the taste of a firstuser of the first device and the taste of a second user of the seconddevice.

The measures may, thus, depend on user dependent information. The userdependent information may comprise a user profile, a user gender, a userage, a user group and/or the like. For example, based on user dependentinformation, it may be determined that the first and second users havethe same or similar age. Thus, the measures may be higher than in caseswhere users have a different age. The reason is that the two users arefound to be similar users and, therefore, are likely to have a similartaste.

At least a part of the measures may also depend on a degree of liking ofa respective content item of the first or second user. For example, thesecond user may have a favorite list which indicates that he/she likesthe content items in the favorite list. This information may be used tocalculate the measures at the first and/or second device. Thus, thedegree of liking may be determined depending on a play list, favoritelist, purchase date, purchase price or the like. The degree of likingmay also be determined depending on a number of times a respectivecontent item has been played since this is also a measure for the degreeof liking of a respective content item. For example, if a user hasplayed a content item many times, he probably likes the content item.Thus, the respective content item may receive a higher score thancontent items that have been played less frequently.

It is possible that the measures depend on a play list or favorite listof a user of the second device. The measures, however, are determined onthe first device. Thus, the second device may transmit to the firstdevice information for each or at least some of the items regardingwhether a respective item belongs to a play list and/or favorite list ofa user of the second device.

The measures may also depend on a comparison of the first and secondidentifiers. The comparison thereby may indicate whether a respectivecontent item is the same or not. If the first or second content itemsare pieces of audio or video data comprising audio information, thefirst and second identifiers may be fingerprints deter-mined inaccordance with European patent application no. 04 028 882.1, thecontents of which is herein incorporated by reference.

The fingerprints are data extracted from the audio information of acontent item and are small in size. The fingerprint may e.g. have a sizeof 291 byte which allows to uniquely identify a song. It is, thereforepossible to exchange the fingerprints between the different devices withlow data traffic. Further, calculation of the fingerprints only requiresvery little processing time. Thus, it is possible to identify thecontent items via the fingerprints which have been calculated on mobiledevices where processing power is low.

Further, since the content items, e.g. pieces of audio, are identifiedby fingerprints, it is not necessary to exchange audio data, e.g.MP3-files or the like, between the different devices. Thus, there is noconflict with copyright laws prohibiting such exchange. Also, nopersonal data need to be exchanged between the first and second devices.Of course, as said, in an embodiment, it is, however, possible to alsoexchange personal data. Still further, it is not necessary that a server(see description of steps 106, 108 below) knows that a certain user hasalready used the server. Each time a user uses the system, a completeanonymous login is possible. It is possible that the method solely worksby an identification via fingerprints.

In a further embodiment, the first and second devices may be mobiledevices, communicating by near-field wireless standards, such as e.g.Bluetooth, Pico-Net, WLAN or the like.

At 106, in FIG. 1, the measures are used to determine and/or update anitem-to-item matrix. This may be done on a third device which isphysically separated from the first and second devices.

The third device may be a server device to which the first and/or seconddevice may connect.

Based on the item-to-item matrix, the third device may determine arecommendation at 108 for a user.

The item-to-item matrix describes the similarity of items with respectto each other depending on usage information collected via the first andsecond devices. Thus, a type of collaborative filtering is done, wherethe data collection is done in a distributed manner on the first andsecond devices. If a large number of first and second devices by a largenumber of user all perform the mentioned tasks, then data collection bythe many users is very powerful. A lot a data can be collected locallyvery easy which improves the item-to-item matrix. Thus, it is desirablethat a large number of users serve as “data collectors” and, thereby,help to update the item-to-item matrix. As is clear from the above, thisis done in a distributed manner on the local devices. Therefore, muchdata is collectable with very few effort.

Therefore, one drawback of collaborative filtering may be overcome,since by collecting the data locally, problems of data sparsity are lesssevere than if the collection was done only at the server.

For example, if the server hosts an online music store, and users ofmobile devices collect data locally, then an item-to-item matrix may beeasily determined which helps to recommend content items (songs) tousers of the online music store.

The item-to-item matrix may therefore be regarded as to have beencreated “piecewise” by a multitude of independent users devices whichinteract with each other, e.g. by near-field communication or byBluetooth or some other means. The interaction itself, thereby, maymerely consist of an exchange of the fingerprints e.g. of a small numberof well liked songs, e.g. frequently played songs, where matches aresearched inside the own local collection, and, if such matches arefound, the other, non-matched fingerprints are accumulated, and, thus,the above-described measures are determined. The measures may,therefore, be referred to as “a tentative local item-to-item similaritymatrix”.

The central site (server=third device) may serve as a “data aggregationunit”. The central site may receive all the tentative item-to-itemmatrices (measures) and combines them into a global item-to-item matrix.The identification of the songs is, thereby, done via fingerprints, andrecommendations are created for the current users based on the globalitem-to-item matrix. The user may then be allowed to downstream parts ofthe recommendations to listen to and to decide whether or not to buy thewhole song (content item).

FIG. 2 shows an embodiment illustrating the interaction of three devicesA, B, and C (first to third devices).

Devices A and B may be portable devices, such as e.g. portable phones,handheld music storage devices or the like. Devices A and B have audiodata bases 200, 202.

At 204 and 206, first and second identifiers are determined for thesongs stored in the audio data bases 200 and 202, respectively. Thefirst and second identifiers may be fingerprints e.g. determined asdescribed in European patent application no. 04 028 882.1. Thus,processing time for determining the first and second identifiers may below and storage required for storing the first and second identifiers isvery little.

At 208, device B sends the calculated second identifiers which aredescriptive for the content items (songs) stored in audio data base 202to device A. Device A receives the second identifiers at 210.

At 212, device A determines measures, also referred to as “itemmeasures”. This may e.g. be done by comparing the first and secondidentifiers. This corresponds to comparing the songs stored in audiodata base 200 and audio data base 202. Device A may, therefore,determine the measures depending on whether the content items in audiodata base 200 are the same as the content items in audio data base 202.It may also be possible to determine the measures depending on a degreeof similarity of the audio data in data bases 200 and 202. Thesimilarity measures may, therefore, be determined based on thefingerprints. For each first identifier, i.e. for each content item indata base 200, a measure may be determined.

The calculated measures together with the associated identifiers (songs)in data base 200, at 214, are then sent to device C, which is e.g. aserver device.

At 216, the item measures and associated first identifiers are received.

At 218, an item-to-item matrix M stored at the device C isdetermined/updated. The item-to-item matrix M may also be referred to as“global item-to-item matrix”.

In order to determine a recommendation, at 220, device A may sent atleast one first identifier to device C, which receives this at least onefirst identifier 1 at 222. Based on this first identifier and based onthe item-to-item matrix M, at 224, a recommendation is determined. At226, this recommendation is sent to device A, which receives therecommendation at 228.

FIGS. 3A and 3B illustrate how the measures (local item-to-itemmatrix/scores) may be calculated. First, at devices A and B, at 300 and302, tags of audio data stored on devices A and B are computed.

In the example of FIG. 3A, on device A, the following tags aredetermined and stored (see reference sign 304): X3, X22, X52, X37, andX502. At device B, the following tags are computed (see reference sign306 in FIG. 3A): X5, X22, X407, X502, X54, X52.

At 308, 310, the tags 304, 306 are exchanged between devices A and B.

Then, at 312, 314, devices A and B compare their own tags with the tagsof the respective other device. Thus, at 312, device A compares its owntags with the tags received from device B. Further, at 314, device Bcompares its own tags with the tags received from device A. It should benoted that it is not necessary that steps 312, 314 be executed on bothdevices. It may be that only device A aims at generating arecommendation or updating its measures. Therefore, it may not benecessary that steps 314, 318 be executed on device B. However, in theexample of FIG. 3A, it is assumed that both devices A and B serve asdata collectors.

At 316 and 318, respective measures are determined.

As seen at 320, in the example of FIG. 3A, the measure (score) of tags(content items) X22, X52, X502 is equal to 1.0. Further, the measuresfor items X3, X37, S5, X407, and X54 is equal to 0.5. As can be seenfrom 304, 306, content items X22, X52, and X502 are commonly ownedcontent items. This is the reason why these content items receive ahigher score. By symmetry, device B does the same thing as evident from318, 322.

At 324, the measures and corresponding tags are sent to the server.

At 326, the server receives the measures and corresponding tags. Themeasures (“local tentative item-to-item matrix”) are then, at 328, usedto update the (global) item-to-item matrix.

FIG. 3B illustrates the updating of measures. In FIG. 3B, the updatemeasures for item X22 in the item-to-item matrix at the server isillustrated.

FIGS. 4A and 4B illustrates an embodiment where the measures depend onfavorite lists of users of devices A and B.

As in FIG. 3A, at 300, 302, tags are computed for all of the contentitems stored at 304, 306.

However, in the embodiment of FIG. 4A, the information stored at 304,306 includes information regarding a degree of liking of certain contentitems of a user of the respective device. In the example of FIG. 4A, theuser of device A has a favorite list 304-FAV (thus implicitly indicatinga degree of liking) including items X3 and X22. Further, the user ofdevice B has a favorite list 306-FAV including items X5 and X22.

In steps 308, 310, in addition to the tags stored at 304, 306, theinformation whether a respective item is included in a favorite list304-FAV, 306-FAV is transmitted/received.

Then, at 312, 314, the own tags and the favorite informationtransmitted/received, is compared with the tags/favorite information ofthe respective other device.

Thus, at 316, 318, the measures are computed depending on a comparisonof the tags stored at 304, 306 as well as depending on whether arespective item is in one of the favorite lists 304-FAV, 306-FAV.

This computation of the measures is illustrated in the table of FIG. 4B.

As can be seen, as an example, the measure (score) of song (item) X3 iscalculated as follows:

Item X3 is owned by A and in favorite list 304-FAV of the user of deviceA. The fact that X3 is owned by A results in adding 0.2 to the score.The fact that X3 is in 304-FAV adds 0.3 to the score resulting in atotal score of 0.5.

The calculation of the scores (measures) for the other items is doneanalogously and will be easily recognized by those skilled in the art.

In the embodiment of FIGS. 4A, 4B, all of the tags stored at 304, 306are exchanged between devices A and B. Thus, devices A and B have thesame information. Therefore, the measures calculated at 316, 318 are thesame for devices A and B.

In contrast, in the embodiment of FIG. 5, not all of the items stored in304, 306 are exchanged. Instead, only the tags of the items in favoritelist 304-FAV, 306-FAV are exchanged at 308-1, 310-1. Steps 308-1, 310-1correspond to steps 308, 310 of FIG. 4A.

Thus, in the embodiment of FIG. 5, devices A and B do not have the sameinformation. Thus, the measures determined at devices A and B may bedifferent.

In FIG. 6, a further embodiment is shown where in addition to taginformation, user data of users of devices A and B are exchanged at 600,602. The user data may include a user profile, user gender, user age,user group and/or the like of a user of the respective device.

In the embodiment of FIG. 6, the measures may, therefore, be determineddepending on the user data.

For example, if it is found that the age of the user of device A and theage of the user of device B is the same or similar, the measures may bedetermined to be higher than in cases where age of users of devices Aand B is very different.

FIG. 7 illustrates how, at a server 700, a recommendation may bedetermined using the item-to-item matrix. At 702, server 700 receivesfrom e.g. device A of FIGS. 3A, 4A, a favorite list 304-FAV.

Server 700, at 704, may then determine values from the globalitem-to-item matrix 706.

At 708, the items with the highest values are searched. In the exampleof FIG. 7, these are items X5, X67 and X76.

At 710, respective audio data corresponding to items X5, X67 and X76 issent to device A.

The audio data X5-D, X67-D, X76-D may only be a part of a correspondingcontent item, i.e. only a part of a song, e.g. 30 seconds or the like.Based on X5-D, X67-D and X76-D the user may then decide to buy the wholecontent item.

FIG. 8 shows an embodiment of a device, e.g. a portable device. Device800 comprises a content item data base, e.g. song data base, 802, a dataprocessor 804, a receiver/transmitter 806, and a display 808.

Receiver/transmitter 806 is adapted to receive second identifiers fromanother device, e.g. device A or B of the FIGS. 3 to 6. The dataprocessor 804 is adapted to determine first identifiers for the contentitems stored in content item data base 802, and further adapted todetermine measures for at least a part of the first content items,wherein each measure is associated with a respective content item.Further, the measure depends on the first and second identifiers.

Receiver/transmitter 806 is adapted to send the measures andcorresponding first content items to a server and, thus, request arecommendation from the server.

When receiving a recommendation, receiver/transmitter 806 and/or dataprocessor 804 may display the recommendation on display 808. In theexample of FIG. 8, a recommendation may be the song “Like a virgin” fromMadonna. The server may, then, transmit only a part of the song to theuser and the user may listen to this part and then decide if he wants topurchase the song or not.

FIG. 9 shows a server device 900 comprising a data processor 902, areceiver/transmitter 904 and a content item data base 906.

Receiver/transmitter 904 may be adapted to receive measures for at leasta part of first content items, wherein each measure is associated with arespective content item, and the measure depends on first and secondidentifiers. The first and second identifiers may have been locallydetermined at first and second devices.

The data processor 902 is adapted to determine or update an item-to-itemmatrix M based on the received measures.

Data processor 902 is further adapted to select a recommendation fromcontent item data base 906 based on item-to-item matrix M and a givencontent item.

As is clear from all of the above, the described embodiments help toobtain customer data and, therefore, provide a stable collaborativefiltering approach which does not suffer from data sparsity.

As explained above, the usage and preference data collection isdecentralized to a multitude of devices. Thus, the main data collectionis not necessarily done at a central server. The central server may onlydo the final merging of the preference data collected locally by e.g.portable devices communicating via some communication channel, e.g.near-field communication.

Thus, according to this approach, an independence of the deliverychannel for the music is achieved. Even if the customer buys the musicin the record store or at an online music store, collaborative filteringdata is collected. Thus, a higher quality of the data is obtained andthe actual usage of content items can be employed rather than only thepurchasing event. (“No I bought this for my aunt, and I do not want itto ruin my profile.”)

Further, there is less server side effort and, there is no need tocollect information in a web shop. Further, since the data collection isdone locally, there is no need to negotiate with web shop owners or tobuild up a web shop at all (online music store).

Also, since all of the identification of data (audio data) is done byfingerprints, a complete anonymous service is possible. This has a highsales impact since the user contacts the service “in need ofrecommendations” which is a particular good moment to present an offerfor a purchase.

Similar to the above-described embodiments, in a further embodiment, thefrequently played songs on a device may be selected and theirfingerprints be computed. Of course, a simple scheme that randomly pickssongs on the device or uses the songs that have been transferred mostrecently to the device or songs that the user have actively selected mayalso be used. In principle, all songs on a device could be used withoutdiscrimination, particularly, if there are not so many of them. The veryfact that a user owns a song and has bothered to put it on a device isalready an indicator that he/she likes a song (degree of liking). Thus,the above-described measures may also depend on when a song has beentransferred to a device.

Once the device of a user A, i.e. a device A, is brought into contactwith a device B of a user B, the two devices may start exchanging theirfingerprints. Then, device A may find out which songs inside the list ofdevice B are inside the preferred item list (favorite list) on device A,and which songs from the list B are stored on device A. For example, ifdevice B gives a list of forty songs to device A, and device A findsseven of them inside the forty most preferred songs of A itself plustwenty more songs which are stored on A (but not on the preferred list),it would conclude, that the music taste of user B is very similar to themusic taste of user A. Conversely, if none of the songs of user B arefound on device A, it would conclude that there is little or nosimilarity between A and B. Then, the information about the similaritybetween B and A is sent to B. By symmetry, B does exactly the samethings.

The received fingerprints of the most preferred songs of B are stored ondevice A along with the computed similarity between A and B. Note thatthe similarity computation involves both, the (locally computed)similarity based on the B list relative to the A content and the(received) similarity based on the A list relative to the B content(which has been computed by device B).

When the user connects to the central server, the information about thefound relationships is sent to the server, namely, the list of device B,the song content of A, together with the similarity rating between A andB. The server then combines all these similarity relationships which areactually parts of the item-to-item similarity table into one largeitem-to-item similarity table (global item-to-item matrix), thusremoving dependencies from the users altogether.

Of course, the devices memorizes not only one other device (B) but asmany as possible, e.g. devices (C, D, E, . . . ).

Although specific embodiments have been illustrated and describedherein, it will be appreciated by those of ordinary skill in the artthat a variety of ultra net and/or equivalent implementations may besubstituted for the specific embodiments shown and described withoutdeparting from the scope of the described embodiments. This applicationis intended to cover any adaptations or variations of the specificembodiments discussed herein. Therefore, it is intended that thisinvention be limited only by the claims and the equivalents thereof.

The invention claimed is:
 1. A method of operating a system forrecommendation of a content item, the system including at least aportable first device, a portable second device, and a third device,comprising: determining, by the first device, first identifiersdescriptive for first content items stored on the first device, thefirst device assigned to a first user, each first identifier beingfingerprint data extracted from audio information contained in one ofthe first content items; determining, by the second device, secondidentifiers descriptive for second content items stored on the seconddevice, the second device assigned to a second user, each secondidentifier being fingerprint data extracted from audio informationcontained in one of the second content items; sending the secondidentifiers from the second device to said first device; determining, bysaid first device, measures for at least a part of the first contentitems, wherein each measure is associated with a respective contentitem, wherein the measure is a value that is higher for the respectivecontent item stored in both the first device and the second device thanfor the respective content item stored in a single one of the first andthe second devices; sending the measures from the first device to saidthird device; determining or updating, on said third device, anitem-to-item matrix based on said measures, wherein said first, second,and third devices are physically separate devices, wherein to eachcontent item the item-to-item matrix assigns a plurality of secondmeasures, and wherein each second measure is assigned to one of othercontent items and is a value that is high for content items stored inboth the first device and the second device, medium for content itemsstored in a single one of the first and the second devices, and low forcontent items stored in neither the first nor the second device; anddetermining, by a processor of the third device, a recommendation forthe first user, the second user, or another user based on said pluralityof second measures assigned to each content item identified by data sentto the third device.
 2. The method of claim 1, wherein said determining,by said first device, said measures includes a comparison ofuser-dependent information stored in the first device and the seconddevice wherein a value of a respective measure is determined to behigher the better the user-dependent information stored in the first andthe second devices match.
 3. The method of claim 2, wherein saiddetermining, by said first device, said measures includes the comparisonof user-dependent information stored in the first device and the seconddevice, wherein said user-dependent information is a user profile, auser gender, a user age, or a user group.
 4. The method of claim 1,wherein said determining) by said first device, said measures includesat least a part of said measures that depends on a comparison of adegree of liking of a respective content item.
 5. The method of claim 4further comprising: determining said degree of liking based on at leastone of a playlist, a favorite list, a purchase date, a purchase price,each being associated with a respective content item, and a number oftimes a respective content item has been played.
 6. The method of claim1, wherein said determining, by said first device, said measuresincludes at least a part of said measures that depends on a playlist ora favorite list of a user of the second device.
 7. The method of claim1, 2, 3, 4, 5, or 6, further comprising: sending the first identifiersfrom the first device to said second device; determining, by said seconddevice, measures for at least a part of the second content items,wherein the measure is a value that is higher for the respective contentitem stored in both the first device and the second device than for therespective content item stored in a single one of the first and thesecond devices; and sending the measures from the second device to saidthird device.
 8. A portable device assigned to a user, comprising: astorage configured to store first content items; a receiver unitconfigured to receive second identifiers descriptive for second contentitems stored on a further device, the further device assigned to afurther user, each second identifier being fingerprint data extractedfrom audio information contained in one of the second content items; adata processor configured to determine first identifiers descriptive forsaid first content items, each first identifier being fingerprint dataextracted from audio information contained in one of the first contentitems, and configured to determine measures for at least a part of saidfirst content items, wherein each measure is associated with arespective content item, wherein the measure is a value that is higherfor the respective content item stored in both the first device and thesecond device than for the respective content item stored in a singleone of the first and the second devices; and a transmitter unitconfigured to send said measures assigned to corresponding first contentitems to a server, and configured to send a request for a recommendationto said server, wherein said receiver unit is further configured toreceive said recommendation.
 9. The portable device of claim 8, whereinsaid data processor is configured to determine at least a part of saidmeasures on the basis of a comparison of user-dependent informationstored in said portable device and said further device, wherein a valueof a respective measure is determined to be higher the better theuser-dependent information stored in the portable device and the furtherdevice match.
 10. The portable device of claim 9, wherein said receiverunit is configured to receive said user-dependent information from saidfurther device, and said user-dependent information includes informationof a user of said further device.
 11. The portable device of claim 9,wherein said receiver unit is configured to receive said user-dependentinformation that includes a user profile, a user gender, a user age, ora user group.
 12. The portable device of claim 8, wherein said dataprocessor is configured to determine at least a part of said measures onthe basis of a comparison of a degree of liking of a respective contentitem.
 13. The portable device of claim 12, wherein said data processoris configured to determine at least a part of said measures on the basisof said comparison, wherein said degree of liking is determined based onat least one of a playlist, a favorite list, a purchase date, a purchaseprice, each associated with a respective content item, and a number oftimes a respective content item has been played.
 14. The portable deviceof claim 13, wherein said receiver unit is configured to receive saiddegree of liking from said further device.
 15. A server device,comprising: a receiver unit configured to receive measures for contentitems, wherein each measure is associated with a respective content itemand depends on first and second identifiers, said first and secondidentifiers being locally determined at a first device assigned to afirst user and at a second device assigned to a second user,respectively, and the first and second identifiers being descriptive forfirst and second content items, respectively, each first identifierbeing fingerprint data extracted from audio information contained in oneof the first content items, each second identifier being fingerprintdata extracted from audio information contained in one of the secondcontent items, wherein the measure is a value that is higher for therespective content item stored in both the first device and the seconddevice than for the respective content item stored in a single one ofthe first and the second devices; and a data processor configured todetermine or update an item-to-item matrix based on said measures,wherein to each content item the item-to-item matrix assigns a pluralityof second measures, wherein each second measure is assigned to one ofother content items and is a value that is high for content items storedin both the first device and the second device, medium for content itemsstored in a single one of the first and the second devices, and low forcontent items stored in neither the first nor the second device, andwherein said receiver unit is further configured to receive a requestfor a recommendation and said data processor is further configured todetermine said recommendation based on said plurality of second measuresassigned to each content item identified by data sent to the serverdevice.
 16. A method for determining an item-to-item matrix with atleast a first device, a second device, and a third device, comprising:determining, by the first device, first identifiers descriptive forfirst content items stored on the first device, the first deviceassigned to a first user, each first identifier being fingerprint dataextracted from audio information contained in one of the first contentitems; determining, by the second device, second identifiers descriptivefor second content items stored on the second device, the second deviceassigned to a second user, each second identifier being fingerprint dataextracted from audio information contained in one of the second contentitems; sending the second identifiers from the second device to saidfirst device; determining, by said first device, measures for at least apart of the first content items, wherein each measure is associated witha respective content item, wherein the measure is a value that is higherfor the respective content item stored in both the first device and thesecond device than for the respective content item stored in a singleone of the first and the second devices; sending the measures from thefirst device to said third device; and determining or updating, by aprocessor of the third device, said item-to-item matrix based on saidmeasures, wherein said first, second, and third devices are physicallyseparate devices, wherein to each content item the item-to-item matrixassigns a plurality of second measures, and wherein each second measureis assigned to one of other content items and is a value that is highfor content items stored in both the first device and the second device,medium for content items stored in a single one of the first and thesecond devices, and low for content items stored in neither the firstnor the second device.
 17. The method of claim 16, wherein saiddetermining, by said first device, said measures includes a comparisonof user-dependent information stored in the first device and the seconddevice, wherein a value of a respective measure is determined to behigher the better the user-dependent information stored in the first andthe second devices match.
 18. A non-transitory computer-readable mediumincluding computer program instructions that respectively cause a firstdevice and a second device to execute a method for recommendation of acontent item comprising: determining first identifiers descriptive forfirst content items stored on the first device, the first deviceassigned to a first user, each first identifier being fingerprint dataextracted from audio information contained in one of the first contentitems; determining second identifiers descriptive for second contentitems stored on the second device, the second device assigned to asecond user, each second identifier being fingerprint data extractedfrom audio information contained in one of the second content items;sending the second identifiers from the second device to said firstdevice; determining, by said first device, measures for at least a partof said first content items, wherein each measure is associated with arespective content item, wherein the measure is a value that is higherfor the respective content item stored in both the first device and thesecond device than for the respective content item stored in a singleone of the first and the second devices; sending the measures to a thirddevice to determine or update, on said third device, an item-to-itemmatrix based on said measures, wherein said first, second, and thirddevices are physically separate devices, wherein to each content itemthe item-to-item matrix assigns a plurality of second measures, andwherein each second measure is assigned to one of other content itemsand is a value that is high for content items stored in both the firstdevice and the second device, medium for content items stored in asingle one of the first and the second devices, and low for contentitems stored in neither the first nor the second device; and receiving arecommendation for the first user.
 19. The non-transitorycomputer-readable medium including computer program instructions ofclaim 18, wherein said determining, by said first device, said measuresincludes a comparison of user-dependent information stored in the firstdevice and the second device, wherein a value of a respective measure isdetermined to be higher the better the user-dependent information storedin the first and the second devices match.